Article
Management
Karl Petter Ulsrud, Anders Helgeland Vandvik, Andreas Breivik Ormevik, Kjetil Fagerholt, Frank Meisel
Summary: This article studies an operational planning problem in the offshore oil and gas industry, aiming to determine routes and sailing speeds for platform supply vessels to minimize costs. The chosen sailing speeds heavily impact the fuel consumption and feasible speed ranges of the vessels, which are influenced by weather conditions. The article proposes a time-discrete mixed integer programming model and an Adaptive Large Neighborhood Search (ALNS) heuristic to solve the problem efficiently.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Marouene Chaieb, Dhekra Ben Sassi
Summary: This paper proposes a hierarchical approach to solve the Home Health Care Scheduling Problem with Simultaneous Pick-up and Delivery and Time Window, which combines clustering algorithm and vehicle routing problem model to generate better solutions in less computation time.
APPLIED SOFT COMPUTING
(2021)
Article
Remote Sensing
Ines Khoufi, Anis Laouiti, Cedric Adjih, Mohamed Hadded
Summary: Unmanned Aerial Vehicles (UAVs), also known as drones, can reduce delivery time and cost, respond to emergencies, and are used for data delivery and collection in dangerous or inaccessible locations. This paper focuses on solving the trajectory planning issue of UAVs by formulating it as a multi-objective optimization problem and demonstrating how to use the NSGA-II algorithm to solve the problem.
Article
Mathematics
Connor Little, Salimur Choudhury, Ting Hu, Kai Salomaa
Summary: The pickup and delivery problem is significant in our interconnected world as it can lead to cost reduction and time savings. This study utilizes a genetic algorithm to solve the multiobjective capacitated pickup and delivery problem, exploring different operations to find optimal solutions.
Article
Computer Science, Interdisciplinary Applications
Xian Yu, Siqian Shen, Babak Badri-Koohi, Haitham Seada
Summary: This research presents a model for solving vehicle routing problems, considering factors such as time window assignments, vehicle routing, and scheduling decisions, and optimizing expected costs under uncertainties, with new customer additions also taken into account.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiaochang Liu, Dujuan Wang, Yunqiang Yin, T. C. E. Cheng
Summary: In this study, we consider the pickup and delivery problem with time windows involving battery-powered electric vehicles under demand uncertainty. We develop a two-stage adaptive robust model to find solutions that are insusceptible to deviations in demands.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Sina Shahnejat-Bushehri, Reza Tavakkoli-Moghaddam, Mehdi Boronoos, Ahmad Ghasemkhani
Summary: This paper introduces a robust optimization model for home health care routing-scheduling problem with uncertain service and travel times, utilizing three meta-heuristic algorithms to solve the issue. Experimental results indicate that the memetic algorithm performs better for large-sized problems, demonstrating the advantage of using a robust model.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Operations Research & Management Science
Amit Kohar, Suresh Kumar Jakhar
Summary: Online food delivery companies arrange pickup and delivery routes to meet customer demands, proposed an enhanced solution to find least cost vehicle routes, and demonstrated through computational experiments its efficiency in solving benchmark problems.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Yuxin Che, Zhenzhen Zhang
Summary: This paper proposes an exact algorithm to solve the vehicle routing problem with simultaneous pickup and delivery, considering stochastic customer demands. The problem is modeled as a two-stage stochastic programming problem with recourse, where routing decisions are made based on known delivery demand and deterministic expected pickup demand in the first stage, and recourse actions are taken in the second stage when stochastic pickup quantities are revealed. Three recourse policies are proposed to deal with failures in the first stage. The Integer L-shaped algorithmic framework is used to solve the problem, and effective lower bounding techniques are designed for the three recourse policies. Computational experiments validate the effectiveness of the proposed algorithm and lower bounding techniques.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Cansu Agrali, Seokcheon Lee
Summary: The Pickup and Delivery Problem with electric vehicles and transfers is introduced in this study, considering practical considerations such as multi-depots, time-windows, and EVs' battery and carrying capacity constraints. A mixed-integer linear programming model is developed to encompass all these constraints, and a hybrid heuristic combining Simulated Annealing and Large Neighborhood Search is proposed to address the computational difficulty of the problem. Experimental results show that our heuristic finds optimal solutions about 90% faster than CPLEX for small instances where CPLEX can find optimal solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Civil
Yong Wang, Xiuwen Wang, Xiangyang Guan, Jinjun Tang
Summary: This study provides tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing and time window assignment strategies. The strategies help optimize resource allocation and utilization, enhancing operational efficiency. Results show that these strategies can optimize recycling services and promote sustainable development in the logistics industry.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Operations Research & Management Science
Mohammed Bazirha, Abdeslam Kadrani, Rachid Benmansour
Summary: Home health care provides a range of medical services for patients at home. This study presents a stochastic programming model with recourse to address the uncertainties and synchronization of services. The objective is to minimize costs and expected values. The deterministic model is solved using various algorithms, while the SPR model is solved using Monte Carlo simulation. Results highlight the complexity of the SPR model compared to the deterministic model.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Yong Wang, Shouguo Peng, Xiangyang Guan, Jianxin Fan, Zheng Wang, Yong Liu, Haizhong Wang
Summary: Collaboration among logistics companies is crucial for enhancing operational efficiency, with the proposed solution improving synchronization of logistics networks and outperforming other algorithms in terms of cost reduction, waiting time, and vehicle numbers.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Mehmet Erdem, Cagri Koc, Eda Yucel
Summary: This paper presents an electric home health care routing and scheduling problem, aiming to minimize the total cost while providing services to patients. By developing an adaptive large neighborhood search heuristic and tailoring it to the specific features of the problem, the paper achieves highly efficient solutions. The study quantifies the advantages of considering different charger technologies and shows that downgrading job competence levels can improve the total cost.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Operations Research & Management Science
Yichen Lu, Chao Yang, Jun Yang
Summary: This paper investigates the application of the truck and drone cooperative delivery model in humanitarian logistics and proposes a multi-objective vehicle routing problem. The problem consists of cooperative routing and supplies allocation subproblems, which are solved using multi-objective optimization methods. Experimental results show that the proposed methods outperform other approaches. In terms of epidemic material delivery, the truck and drone cooperative delivery model has advantages. Sensitivity analysis reveals the impact of different drone parameters on delivery efficiency.
ANNALS OF OPERATIONS RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)